Numerous remote target applications, such as high-bandwidth communications and long distance imaging systems, now use over-the-air optical signals for information transfer. Tactical high-energy laser (HEL) systems, for example, communicate optical signals over large distances for object identification and aimpoint controls in laser guided weapons systems. These systems allow operators to identify objects from distances large enough to protect operator safety, but close enough to allow somewhat accurate identification.
Despite showing promise, there are limitations to using optical signals in these applications. One limitation is the effect the atmosphere has on optical signals. In a targeting system, for example, a laser beam may travel long distances through turbulent air before the laser beam reaches its target. This air turbulence can produce a twinkling or blurring effect that degrades the laser beam. The beam distortion and expansion that results from this turbulence alters the wavefront on the laser beam and prevents proper beam focusing, which in turn can mean that the detected image may be indiscernible or unusable.
A few adaptive optics techniques have been proposed to correct for the wavefront anomalies experienced by a laser beam or an optical signal, in general, during propagation. These techniques, however, are only as accurate as the wavefront sensors they employ. Hartmann wavefront sensors, which measure wavefront distortion using point sources as a reference beam against an obtained image, are problematic because they require the generation of a separate reference beam, if a star or point source is not immediately available. This requirement adds to device complexity and can introduce additional sources of scattered light.
In contrast to these point source-based techniques, phase diversity techniques use an extended scene to determine the wavefront anomalies created by the optical aberrations of the imaging system or propagation path. Phase diversity algorithms use at least two images. In the case of the two-image phase diversity configuration, one image contains an additional known aberration with respect to the other image. Phase diversity can be used to correct for blurriness in an image as may result from atmospheric anomalies and thermal blooming. Yet, current phase diversity algorithms require time-consuming microprocessor processing. These microprocessor systems are programming intensive at the front end and, due to the sequential nature of code execution and other factors, too slow at the back end when the algorithm is executed. No matter the processing speed of the microprocessor core, such systems may take seconds or minutes to complete a full phase diversity correction cycle of an object image. This is too slow for many applications, including real time imaging.